Ploomber
A framework to build modular, collaborative, and production-ready data pipelines that integrates seamlessly with Jupyter and other editors.
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Product Overview
What is Ploomber?
Ploomber is designed to simplify the development and deployment of data science and machine learning pipelines by enabling users to convert scripts, notebooks, or functions into maintainable pipelines. It solves the common problem of notebook refactoring by allowing teams to prototype in Jupyter notebooks and then deploy without breaking workflows. Ploomber supports Python, SQL, and notebook tasks, tracks code changes to optimize execution, and can be deployed on various platforms including Kubernetes and cloud environments.
Key Features
Modular Pipeline Construction
Convert collections of scripts, notebooks, or functions into pipelines with clear task dependencies and outputs.
Seamless Jupyter Integration
Develop interactively using Jupyter notebooks or any editor, then deploy pipelines without rewriting code.
Incremental Execution
Automatically caches results and re-executes only tasks whose source code has changed, speeding up development cycles.
Multi-Environment Deployment
Deploy pipelines locally or on distributed systems like Kubernetes, Airflow, AWS Batch, or SLURM with zero code changes.
Legacy Notebook Refactoring
Automatically convert monolithic notebooks into modular, maintainable pipelines.
Extensive Task Support
Supports Python functions, scripts, notebooks, and SQL scripts within the same pipeline.
Use Cases
- Data Science Workflow Automation : Streamline data processing and model training pipelines with modular, reusable components.
- Collaborative Machine Learning Development : Enable teams to prototype, share, and deploy pipelines collaboratively without breaking code.
- Legacy Notebook Modernization : Transform existing Jupyter notebooks into production-ready pipelines for better maintainability.
- Scalable Pipeline Deployment : Run pipelines on local machines or scale to cloud and cluster environments effortlessly.
- Incremental Pipeline Execution : Optimize development speed by only rerunning changed pipeline components.
FAQs
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